{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rpy2.robjects as robjects\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "from common_tools import r_import_tool as r_in\n",
    "from common_tools import plotting_tools as ploto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model mc and model m3 data\n",
    "robjects.r['load']('./data/modeldata_mc_out.RData')\n",
    "model_mc = r_in.import_rdata('data_mc',1,'$m_{c}$')\n",
    "\n",
    "robjects.r['load']('./data/modeldata_m3_out.RData')\n",
    "model_m3 = r_in.import_rdata('data_m3',1,'$m_{3}$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 350x175 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom = {\"grid.linestyle\": \"dashed\", \"grid.color\": \"lightgrey\"}\n",
    "sns.set_theme(style=\"ticks\", rc = custom)\n",
    "blue_palette = sns.color_palette(\"light:b\")\n",
    "purple_palette = sns.color_palette(\"flare\")\n",
    "\n",
    "# Create a palette dictionary matching the model names in the data\n",
    "palette = [blue_palette[4], purple_palette[4]]\n",
    "\n",
    "# Create a dataframe with b_T_Sol values\n",
    "data_b_C_S_coll = pd.concat([model_m3[['b_C_S','model']],  \n",
    "                        model_mc[['b_C_S','model']]],\n",
    "                       ignore_index=True)\n",
    "\n",
    "# Plot values\n",
    "ploto.plot_pretty_boxplot(data_b_C_S_coll, \"b_C_S\", \"b_C_S_coll\", 0.25, 0.50, '${b}_{c,s}$', palette)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mercury",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
